Marginal coverage is a lie of averages: the conformal diagnostics that catch it
Article summary
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Disclaimer: This article was drafted with AI assistance and reviewed and edited by the author. The technical design and opinions are my own. You wrapped your classifier in a conformal predictor, calibrated it for 90% coverage, checked the held-out set, and saw 90.2%. Ship it. That number is real — and it can still be hiding a model that badly under-covers exactly the cases you care about. Marginal coverage is an average, and averages launder failure. This is a different problem from conformal…
1Key Takeaways
- Disclaimer: This article was drafted with AI assistance and reviewed and edited by the author.
- The technical design and opinions are my own.
- You wrapped your classifier in a conformal predictor, calibrated it for 90% coverage, checked the held-out set, and saw 90.2%.
- That number is real — and it can still be hiding a model that badly under-covers exactly the cases you care about.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that disclaimer: This article was drafted with AI assistance and reviewed and edited by the author.
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